Shadow removal AI is stuck because training datasets are outdated. Models advance, but real-world application falters.
FO Take · Score 75
AI’s much-vaunted progress is a house of cards built on stale data. We celebrate algorithmic leaps while ignoring the foundational rot of unchanged training sets. This isn’t innovation; it’s iterating on obsolescence. Until we tackle the messy reality of data acquisition, AI will remain a lab curiosity, not a world-changer. Will AI ever truly escape its training shadows, or is it doomed to repeat past data biases?
The strongest counter
AI progress is often iterative, building on established datasets. Real-world applications follow as models mature. Data collection is complex and costly, making incremental updates logical.
